Temporomandibular disorders (TMD) are common in dentistry, with complex etiologies and diverse clinical manifestations. Imaging techniques allow visualization and precise quantification of temporomandibular joint (TMJ) structures. MRI can reveal soft tissue abnormalities by analyzing disc morphology, degree of displacement, joint effusion, and lateral pterygoid muscle attachment patterns; while CT provides quantitative parameters of bony structures such as condylar morphology, size, and position. This review summarizes the associations between CT and MRI structural features of the TMJ with TMD severity and prognosis, aiming to provide a theoretical basis for precision diagnosis and treatment of TMD.

Neurovascular coupling (NVC) refers to the brain's regulatory capacity to adjust cerebral blood flow in response to neuronal activity, representing a coordinated function between neural activity and hemodynamics. Impairment in NVC diminishes connectivity between distinct brain regions, thereby impairing cognitive function. Currently, various neuroimaging techniques including arterial spin labeling, resting-state functional MRI, and functional near-infrared spectroscopy are employed to evaluate NVC function. These methods have confirmed that declines in NVC are closely linked to cognitive impairments in conditions such as vascular cognitive impairment, Alzheimer's disease, diabetic cognitive decline, and cognitive deficits in end-stage renal disease patients. This review aims to provide an overview of recent advances in neuroimaging research on the relationship between NVC and cognitive disorders.

Metal implants have been widely used in various surgical procedures. These metal implants generate artifacts during CT examinations, which can compromise the assessment of surrounding tissues such as muscles and bones. In clinical practice, various reconstruction and post-processing techniques are commonly employed to reduce the impact of metal artifacts on lesion evaluation. In recent years, photon-counting computed tomography (PCCT), with its technical characteristics such as high spatial resolution and multi-energy bin capability, has demonstrated distinct advantages in metal artifact reduction. This paper briefly outlines the principles and advantages of PCCT, introduces the main metal artifact reduction techniques currently available, and reviews the recent progress of its clinical applications in the musculoskeletal system, cardiovascular and cerebrovascular system, head and neck, and nervous system.

Musculoskeletal disorders are characterized by diverse pathological types and complex anatomical structures, placing high demands on the precision of imaging examinations. In recent years, artificial intelligence (AI) has shown great potential in musculoskeletal imaging, particularly in anatomical segmentation, lesion detection, quantitative measurement, and intelligent diagnosis. This review systematically summarizes advances in AI applications for degenerative joint diseases, sports injuries, fracture detection, osteoporosis screening, and musculoskeletal tumors, while also outlining its expanding roles in image reconstruction, quality control, and educational support. Furthermore, it highlights the developmental trends of large AI models in musculoskeletal imaging and discusses their potential and challenges in multimodal, multitask, and personalized clinical decision support. Although AI has reached or even surpassed expert-level performance in certain tasks, limitations remain in model generalization, data acquisition and annotation standards, cross-modality integration, and clinical adaptability. Future progress will require high-quality data construction, interdisciplinary collaboration, and the establishment of standardized frameworks to advance musculoskeletal AI toward more intelligent, efficient, and standardized clinical practice.

Fetal and childhood periods are critical stages of early brain development. The advancement of multimodal MRI technologies has provided important tools for investigating brain development and neurodevelopmental disorders. Structural MRI can reveal brain volume and cortical developmental patterns; diffusion MRI can delineate white matter tracts and the development of neural networks; blood oxygenation level dependent (BOLD) functional MRI can elucidate the formation and abnormalities of functional networks; and MRS can reflect the metabolic states of the brain. This review summarizes recent progress in the use of multimodal MRI to investigate fetal and pediatric brain development, as well as its applications in the early diagnosis, disease subtyping, and exploration of comorbidity mechanisms of neurodevelopmental disorders such as attention-deficit/hyperactivity disorder, autism spectrum disorder, and Tourette syndrome.

Ischemia and hypoxia caused by cerebral vascular stenosis or occlusion constitute the core pathological mechanisms of ischemic stroke. As an important compensatory pathway for blood supply in the body, collateral circulation directly influences the survival time of the ischemic penumbra, the rate of infarct expansion, and the long-term prognosis of patients. This article reviews and analyzes the classification of cerebral collateral circulation and its related hemodynamic basis, CT and MRI-based evaluation of collateral circulation, as well as the pathological basis of collateral circulation in ischemic stroke and its correlation with imaging findings. It is anticipated that future imaging evaluation of collateral circulation will develop toward greater precision, multimodality integration, and intelligence, thereby providing stronger support for individualized patient treatment.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by insidious early symptoms, rapid progression, and extremely poor prognosis. The Chinese Guidelines for the Diagnosis and Treatment of Pancreatic Cancer (2022 Edition) emphasize the importance of early screening and precision imaging assessment in high-risk populations, and recommend MRI as an important modality for screening and diagnosis. In conjunction with the epidemiological characteristics of PDAC in China and the key points of domestic and international expert consensus, this article systematically interprets the latest international MRI screening protocols and reporting templates. The aim is to improve the early detection rate of PDAC, improve patient prognosis, and provide evidence-based support for the continuous refinement of domestic guidelines.

Vulnerable carotid plaques and cerebral small vessel disease (CSVD) are both significant etiologies of ischemic stroke, and they are closely interrelated. Imaging modalities, including ultrasound, CT angiography (CTA), and MR high-resolution vessel wall imaging (HR-VWI), enable the assessment of various vulnerable plaque characteristics. This review focuses on the correlation between vulnerable plaques and CSVD, aiming to provide a theoretical basis for further research and clinical application.

Transcranial magnetic stimulation (TMS) is a non-invasive physical therapy technique that has demonstrated clear efficacy in treating various neurological diseases. However, its therapeutic efficacy varies significantly between individuals, and precise localization of personalized stimulation targets is critical to improving efficacy. Structural MRI can guide TMS by accounting for individual differences in brain anatomy. Functional MRI can identify individualized coordinates of brain function and abnormal brain activity, providing personalized stimulation targets for TMS. Resting-state fMRI functional connectivity is believed to provide a “bridge”, enabling magnetic stimulation to propagate from the cortical surface to deep effect targets. This article introduces the progress of MRI in the precise localization of TMS targets.

Gastric cancer is one of the malignancies with high incidence and mortality worldwide. Neoadjuvant immunotherapy has significantly improved the prognosis patients with gastric cancer, and it is of great clinical significance to accurately evaluate the efficacy of neoadjuvant immunotherapy at an early stage. Conventional imaging modalities, such as CT and MRI, have demonstrated value in evaluating the efficacy of neoadjuvant immunotherapy. In addition, artificial intelligence models can be widely applied to predict responses to neoadjuvant immunotherapy in gastric cancer and have shown good performance. This article reviews the research progress in the evaluation of neoadjuvant immunotherapy efficacy using conventional imaging modalities and artificial intelligence models in gastric cancer.

Objective To evaluate the setup accuracy of surface-guided radiotherapy (SGRT) in hypofractionated radiotherapy for breast cancer, to quantify the relationship between setup errors and target dose distribution, and to determine setup error control thresholds according to different planning target volume (PTV) sizes. Methods Sixty female patients with breast cancer who underwent breast-conserving surgery and completed hypofractionated radiotherapy were retrospectively enrolled. According to the treatment guidance method, patients were divided into a conventional setup group (n=30) and an SGRT group (n=30). Setup errors between the two groups were compared using the Mann-Whitney U test. Spearman correlation analysis was performed to assess the association between SGRT-derived setup errors and cone-beam computed tomography (CBCT) registration errors, followed by linear fitting using ordinary least squares regression. Dose variations caused by simulated setup errors were evaluated using the Eclipse treatment planning system. Pearson correlation analysis was applied to investigate the linear relationship between PTV volume and actual delivered dose under different setup error conditions. Results Setup errors along the X, Y, and Z axes in the SGRT group were significantly smaller than those in the conventional group (all P<0.05), and the three-dimensional setup errors of all SGRT cases were <0.4 cm. SGRT-derived setup errors showed a strong positive correlation with CBCT-based registration results (r=0.81-0.91, all P<0.001), demonstrating high consistency between the two methods. Dosimetric simulation revealed that when the overall setup error exceeded 0.4 cm, target dose deviations increased markedly, with the Z-axis showing the highest sensitivity. Significant axial differences were observed in the linear correlation between PTV volume and delivered dose. For the Z-axis, a moderate correlation was identified at setup errors of 0.4 cm and ±0.5 cm, and a weak correlation at -0.4 cm. For the X-axis, a weak correlation was observed at -0.4 cm and -0.5 cm, whereas no significant linear correlation was found for the Y-axis at any setup error level. Notably, in patients with PTV <400 cm³, the Z-axis setup error should be controlled within 0.2 cm. Conclusion SGRT provides accurate, safe, and non-invasive guidance for hypofractionated radiotherapy in breast cancer, reducing radiation exposure and patient psychological burden while improving workflow efficiency. Setup error thresholds along the Z-axis should be individualized based on PTV volume to ensure optimal dose delivery.

Objective To investigate a model based on MRI habitat imaging analysis of axillary lymph node (ALN) heterogeneity combined with clinicopathological features for assessing the response to neoadjuvant therapy (NAT) in breast cancer patients with ALN positivity (ALN+). Methods A total of 369 female patients with pathologically confirmed ALN+ breast cancer were retrospectively enrolled and randomly divided into a training set (n=259) and a validation set (n=110) in a 7∶3 ratio. All patients underwent dynamic contrast-enhanced MRI before treatment. Stable 3D radiomic features were used, and the optimal number of clusters was determined using Gaussian mixture modeling and the Bayesian information criterion to generate habitat imaging and extract subregional features. Four support vector machine (SVM)-based models were constructed: a clinical model, a habitat radiomics model, an ALN heterogeneity score model, and a late-fusion combined model. Predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) values were compared using the Delong test. Clinical utility was assessed using decision curve analysis. Results The clinical model was constructed based on PR status, HER2 status, and Ki-67 index. The habitat radiomics model was developed using 17 non-zero radiomic features extracted from four subregions. An ALN heterogeneity score model and a combined model were also established. In the validation set, the AUCs of the clinical model, habitat radiomics model, ALN heterogeneity score model, and combined model were 0.79, 0.60, 0.69, 0.85, respectively. The Delong test showed that the AUC of the combined model was significantly higher than those of all individual models (all P<0.05). Decision curve analysis demonstrated that the combined model consistently provided a high net benefit within the threshold probability range of 0.2-0.8, indicating favorable clinical applicability. Conclusion A combined model integrating MRI-based habitat radiomics features, ALN heterogeneity scores, and clinicopathological characteristics may assist in evaluating post-NAT lymph node status and support individualized clinical decision-making.

Rheumatic diseases (RD) are a group of systemic disorders affecting multiple systems, with chronic inflammatory responses in vascular and connective tissues as their main pathological basis. Due to its sensitive detection of metabolic activity and systemic inflammatory burden, ¹⁸F-FDG PET/CT, has become increasingly important for early diagnosis, assessment of disease activity, and monitoring of treatment in RD. This article reviews the research progress of ¹⁸F-FDG PET/CT in various RD, including rheumatoid arthritis, polymyalgia rheumatica, idiopathic inflammatory myopathies, adult-onset Still’s disease, systemic lupus erythematosus, IgG4-related disease, relapsing polychondritis, Sjögren’s syndrome, spondyloarthritis, and Takayasu arteritis,etc.

Objective To investigate the association between different skull fracture sites and the occurrence and complexity of intracranial hemorrhage in patients with traumatic brain injury (TBI), and to develop CT-based risk prediction models for intracranial hemorrhage using initial head computed tomography (CT) findings. Methods In this multicenter retrospective study, 4 700 TBI patients from five tertiary hospitals were enrolled. Based on the initial CT findings, patients were categorized into three groups: no intracranial hemorrhage (n=1 602), single intracranial hemorrhage (n=1 011), and multiple intracranial hemorrhages (n=2 087). Data on age, sex, scalp hematoma, midline shift, cerebral herniation, skull fracture sites, and intracranial hemorrhage types were recorded. Continuous variables were compared among groups using one-way analysis of variance (ANOVA), while categorical variables were compared using the chi-square test. Univariable analysis was used to assess the associations between skull fracture sites and various types of intracranial hemorrhage. Multivariable logistic regression was then performed to identify independent risk factors related to skull fracture sites for predicting the occurrence and complexity of intracranial hemorrhage.Prediction models for the presence/absence of intracranial hemorrhage and hemorrhage complexity were constructed accordingly, and model performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). Results Significant differences were observed among the three groups in age, sex, scalp hematoma, midline shift, cerebral herniation, and the distribution of skull fracture sites (all P<0.05). Cranial fractures were identified as risk factors for intracranial hemorrhage, with sphenoid fractures (OR=8.35) and temporal fractures (OR=6.93) showing the strongest associations. In the model predicting the presence or absence of intracranial hemorrhage, after incorporating clinical information and specific skull fracture sites, the model achieved an AUC of 0.895 (95%CI: 0.89-0.90), demonstrated good calibration, and DCA indicated a high net benefit within a threshold probability range of approximately 0.10-0.60. The prediction model for multiple intracranial hemorrhages had an AUC of 0.689 (95%CI: 0.67-0.71), showed acceptable calibration, and retained some clinical utility within certain threshold probability ranges. Furthermore, epidural hematoma (EDH) was significantly associated with temporal and sphenoid fractures, as well as high-risk combined cranial fracture patterns (all P<0.05). Conclusion Skull fracture sites are closely associated with the occurrence and complexity of intracranial hemorrhage in TBI patients. The risk prediction models for intracranial hemorrhage, constructed based on skull fracture sites and relevant clinical information, exhibit good discriminative ability and have certain clinical decision-making value, which may serve as a reference for early risk stratification of TBI patients in the emergency department.

Objective To compare the clinical value of true electrocardiogram (ECG)-gating versus virtual ECG-gating in coronary computed tomography angiography (CCTA). Methods A total of 108 patients with suspected coronary artery disease undergoing CCTA were prospectively enrolled. Because switching between the two gating modes required additional time, participants were alternately assigned by the examination date to the true ECG-gating group (n=54) and the virtual ECG-gating group (n=54). Scans were performed on a United Imaging uCT 960+ wide-detector CT. Examination time and radiation dose were documented. Using the American Heart Association 18-segment model, 5-point subjective image quality scores were obtained, and coronary artery CT attenuation values, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured. For patients who underwent invasive coronary angiography (ICA) within 30 days after CCTA, diagnostic concordance was assessed with ICA as the gold standard, with subgroup analysis by scanning mode. Continuous variables were compared using the t-test or Mann-Whitney U test, and categorical variables with the chi-square test. Receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic performance. Results In the virtual ECG-gating group, the distal left circumflex artery (LCX) CT attenuation, SNR, and CNR, as well as the subjective image quality score of the posterior descending branch of the right coronary artery, were superior to the true-gating group (all P<0.05). No statistically significant differences were found for other image quality parameters between groups (all P>0.05). Compared with the true-gating group, the virtual ECG-gating group had a 20.5% higher effective dose [3.99 (3.77, 4.28) mSv vs. 3.31 (2.91, 3.75) mSv, P<0.001], a 24.7% shorter total examination time [(3.62±0.34) min vs. (4.81±0.54) min, P<0.05], and a 61% shorter positioning time [(0.76±0.19) min vs. (1.95±0.36) min, P<0.05], but a 14.6% longer image reconstruction time [(48.06±7.18) s vs. (41.93±8.90) s, P<0.05]. In the ICA-validated subgroup, diagnostic concordance rates were 79.17% in the true ECG-gating group and 92.86% in the virtual ECG-gating group; the difference in diagnostic performance was not statistically significant (AUC: 0.792 vs. 0.854, P>0.05). Conclusions Virtual ECG-gated CCTA significantly improves efficiency while maintaining image quality and diagnostic performance, with a slight increase in radiation dose. It is recommended for time-sensitive clinical scenarios, with adherence to dose-optimization principles.

Objective To explore the injury patterns resulting from different injury mechanisms of the first metatarsophalangeal joint (MTPJ) and to characterize their magnetic resonance imaging (MRI) features, thereby providing accurate imaging evidence for early diagnosis and treatment. Methods This retrospective study included 84 patients (84 feet) with first MTPJ injuries confirmed by surgery or clinical follow-up, with a mean age of 39.8 ± 9.5 years. All patients underwent MR examinations. Injury patterns were analyzed according to the underlying injury mechanisms, and corresponding MRI features were evaluated. Interobserver agreement between two musculoskeletal radiologists for the diagnosis of first MTPJ injuries was assessed using Kappa statistics. Results Based on the injury mechanism, 68 patients sustained plantar plate injuries caused by isolated hyperextension, 6 patients had medial collateral ligament injuries resulting from isolated valgus stress, 7 patients had lateral collateral ligament injuries caused by isolated varus stress, and 3 patients sustained combined valgus and hyperextension injuries. On MRI, injuries involving the central portion of the plantar plate, intersesamoid ligament, metatarsosesamoid ligament, main collateral ligament, accessory sesamoid ligament, and partial-thickness tears of the sesamophalangeal ligament were characterized by irregular morphology and focal hyperintensity on proton density-weighted fat-suppressed images. Full-thickness tears of the sesamophalangeal ligament were manifested as complete disruption of ligament continuity, with the tear extending through the entire ligament. Interobserver agreement for the diagnosis of first MTPJ injuries was good to excellent (all kappa>0.7). Conclusion MRI allows clear visualization of injury patterns and characteristic imaging findings of first MTPJ injuries and enables accurate classification of injury types. It plays an important role in the early diagnosis and precise management of first MTPJ injuries.

Ultrafast real-time MRI is a magnetic resonance imaging technique capable of monitoring dynamic physiological processes in real time, while offering both high image quality and high temporal resolution. This technique can depict the dynamic movement of contents at the esophagogastric junction, enable monitoring of reflux events and hiatal hernia, and improve diagnostic accuracy for gastroesophageal reflux disease when combined with morphological parameters of the esophagogastric junction. Additionally, it shows clinical potential in preoperative planning for hiatal hernia repair and in postoperative follow-up after fundoplication. Ultrafast real-time MRI provides a novel adjunctive diagnostic approach for gastroesophageal reflux disease. This review summarizes the recent advances in the application of ultrafast real-time MRI in gastroesophageal reflux disease.

The occurrence, progression, and metastasis of breast cancer are closely associated with the tumor microenvironment. Habitat imaging (HI) can characterize the tumor microenvironment by reflecting intratumoral heterogeneity (ITH), and it holds significant value in assisting the evaluation of molecular characteristics, subtype differentiation, predicting tumor treatment response, assessing metastasis risk, and prognostic prediction in breast cancer. This article briefly explains the principles and methods of HI, reviews the research progress of HI in breast cancer, and analyzes current challenges and future directions, aiming to provide references for the precise diagnosis and personalized treatment of breast cancer.

Malignant perivascular epithelioid cell tumor(PEComa) of soft tissue is an extremely rare mesenchymal neoplasms exhibits certain characteristic imaging features on ultrasound, CT, and MRI. However, it is important to differentiate it from other mesenchymal tumors within soft tissue that containing fat or melanin components,such as clear cell sarcoma, liposarcoma and malignant melanoma. This paper summarizes the imaging characteristics of malignant soft tissue PEComa to assist in clinical diagnosis and differential diagnosis.

Carotid atherosclerotic disease is one of the primary causes of ischemic stroke. Artificial intelligence-based quantitative plaque analysis using computed tomography angiography (CTA) enables automated segmentation and quantification of plaque components, facilitating early prevention and management of ischemic stroke. This article reviews the principle and key parameters of CTA-based quantitative plaque analysis, the detection and segmentation of carotid plaques, and focuses on the application value of this technology in the diagnosis and treatment of carotid atherosclerosis, including plaque quantification and comprehensive assessment of hemodynamic environment, identification of vulnerable plaques, prediction of ischemic stroke recurrence risk, and monitoring and evaluation of treatment efficacy. The limitations of CTA-based quantitative plaque analysis and potential future developments are also discussed.

Objective To develop a nomogram based on subjective features from contrast-enhanced CT and to evaluate its ability to predict the metastatic risk of enlarged regional lymph nodes after neoadjuvant immunochemotherapy (nICT) in patients with esophageal squamous cell carcinoma (ESCC). Methods This retrospective multicenter study enrolled 60 patients with locally advanced ESCC who underwent nICT followed by surgical resection at three medical centers. A total of 81 radiologically enlarged regional lymph nodes identified on post-nICT CT scans with matched pathological results were analyzed. Long-axis diameter (LAD), short-axis diameter (SAD), and multiple radiologic features of lymph nodes were evaluated. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of nodal metastasis and to construct a nomogram. Model performance was assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC), with DeLong’s test used for comparisons. Internal validation was conducted using bootstrap resampling. Model discrimination, calibration, and clinical utility were evaluated using the concordance index (C-index), calibration curves, and decision curve analysis (DCA), respectively. Results Multivariate analysis identified a smaller percentage change in LAD (ΔLAD), the presence of radiologic extranodal extension (iENE), and loss of kidney-shaped morphology as independent predictors of lymph node metastasis. The nomogram demonstrated excellent predictive performance, with a C-index of 0.903 and a bootstrap-corrected C-index of 0.893. The AUC of the nomogram was 0.903, with a sensitivity of 90.0% and specificity of 78.7%, significantly outperforming any single imaging feature alone (all P<0.05). Calibration curves showed good agreement between predicted probabilities and observed outcomes. DCA indicated that the combined model provided a higher net benefit across a wide range of threshold probabilities (0.10-0.75). In addition, a ΔLAD cutoff value greater than 27% effectively differentiated reactive nodal enlargement from metastatic nodes, with a sensitivity of 75.0% and specificity of 63.9%. Conclusion The CT-based nomogram developed in this study enables reliable preoperative prediction of metastatic status in enlarged regional lymph nodes after nICT in ESCC patients. This model may serve as a practical imaging tool to distinguish immune-related pseudoprogression from true nodal metastasis and to facilitate individualized treatment planning.

Bladder cancer is a urogenital system malignancy with high incidence and mortality. With the rapid development of artificial intelligence(AI), AI technologies based on machine learning and deep learning have been applied to the imaging diagnosis of bladder cancer, demonstrating potential clinical value particularly in automatic image segmentation, clinical staging, pathological diagnosis, prediction of relevant protein expression, assessment of chemotherapy efficacy, and prognosis evaluation. This article reviews the research progress of AI in the imaging evaluation of bladder cancer.

Objective To investigate the predictive value of quantitatively assessing perirenal and abdominal fat using the mDixon-Quant technique for early renal function changes in patients with type 2 diabetes mellitus (T2DM). Methods Seventy-five patients with T2DM (mean age, 49.65±13.45 years) were prospectively enrolled. According to the urinary albumin creatinine ratio, patients were divided into a normal albuminuria (NAU) group (n=44) and a microalbuminuria (MAU) group (n=31). In addition, 41 age- and sex-matched healthy volunteers were enrolled as a control group. All subjects underwent abdominal mDixon-Quant MRI scanning. Perirenal fat thickness (PRFT) of both kidneys, visceral fat area (VFA), and subcutaneous fat area (SFA) were measured. Independent sample t-test and Mann-Whitney U test were used for comparison between the two groups, while one-way analysis of variance, Kruskal-Wallis test, and chi-square test were used for comparison among three groups. Logistic regression analysis was applied to identify factors associated with early diabetic renal function changes. Pearson or Spearman correlation analyses were used to assess the relationships between total PRFT and clinical characteristics. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the performance of total PRFT, and the area under the curve (AUC) was calculated. Results Compared with the control group, body weight, body mass index (BMI), systolic blood pressure, diastolic blood pressure, VFA, SFA, L-PRFT, R-PRFT, and total PRFT were significantly increased in both the NAU group and the MAU group (all P<0.05). Compared with the NAU group, the MAU groups showed significantly lower estimated glomerular filtration rate (eGFR) and diastolic blood pressure (both P<0.05), while the proportion of males, systolic blood pressure, serum creatinine (SCr), and urea-to-creatinine ratio (UCR) were significantly higher (all P<0.05). Multifactorial logistic regression analysis showed that diastolic blood pressure and total PRFT were independent associated with NAU status in patients with T2DM (P<0.05), and total PRFT remained an independent factor after adjusting diastolic blood pressure (P<0.05). Total PRFT was also an independent associated with MAU status in patients with T2DM (P<0.05). Body weight and BMI were positively correlated with total PRFT (r=0.642 and 0.616, respectively, both P<0.001). ROC curve analysis demonstrated that total PRFT had good predictive performance for NAU and MAU status, with AUC values of 0.822 and 0.810, respectively. Conclusions The mDixon-Quant technique enables noninvasive and quantitative assessment of perirenal and abdominal fat. Perirenal fat, in particular, shows predictive value for early renal function changes in patients with type 2 diabetes mellitus.

Pancreatic cancer is a highly malignant tumor of digestive system. Radiomics enables non-invasive, comprehensive, and quantitative analysis of tumor heterogeneity in pancreatic cancer, thereby improving lesion prediction and diagnostic accuracy. Deep learning, through the automatic extraction of deep features from images, further enhances the effectiveness of images in diagnosis and prediction. This review summarizes recent advances in the application of radiomics and deep learning techniques to pancreatic cancer, covering early screening and diagnosis, preoperative staging and metastasis assessment, differential diagnosis, treatment response evaluation, and recurrence and prognosis prediction.

Objective To explore the value of multiparametric MRI (mpMRI) in detecting clinically significant prostate cancer (csPCa) in patients with biopsy Gleason score 6 prostate cancer. Methods A retrospective study was conducted including 110 patients with biopsy-confirmed prostate cancer and a Gleason score of 6. The mean age was 72.3±7.2 years. All patients underwent mpMRI prior to prostate biopsy, and lesions were assessed using the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1. Postoperative pathological findings served as the reference standard for csPCa, defined according to the Epstein criteria. Patients were divided into csPCa and non-csPCa groups. Clinical and imaging variables were compared using the Mann-Whitney U test, chi-square test, or Fisher’s exact test, as appropriate. Variables showing statistical significance were entered into multivariate logistic regression analysis to identify independent predictors of csPCa. Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic performance, and the area under the curve (AUC) was calculated. Results Among the 110 patients, 92 (83.6%) were confirmed to have csPCa on postoperative pathology. Compared with the non-csPCa group, patients in the csPCa group had significantly larger tumor diameters and a higher proportion of tumor stage ≥T₃ and PI-RADS scores ≥4 (all P<0.05). Multivariate logistic regression analysis identified tumor diameter as the only independent predictor of csPCa (P<0.05). ROC analysis demonstrated that tumor diameter showed the highest diagnostic performance (AUC=0.90), followed by PI-RADS v2.1 score (AUC=0.75) and tumor stage ≥T₃ (AUC=0.63). Tumor diameter achieved the highest sensitivity (95.65%), accuracy (92.52%), and negative predictive value (73.33%), whereas tumor stage ≥T₃ demonstrated the highest specificity and positive predictive value (both 100%). Conclusion mpMRI is valuable for detecting potential csPCa in patients with biopsy Gleason score 6 prostate cancer.

Radiation-induced brain injury (RIBI) is a common complication following radiotherapy for nasopharyngeal carcinoma, often leading to cognitive impairment and other neurological sequelae. Multimodal MRI, including perfusion-weighted imaging (PWI), susceptibility-weighted imaging (SWI), blood oxygen level dependent-functional MRI (BOLD-fMRI), diffusion MRI, voxel-based morphometry (VBM), and magnetic resonance spectroscopy (MRS), provides multidimensional insights into the pathophysiological mechanisms of RIBI by revealing vascular damage, neural dysfunction, structural injury, and metabolic abnormalities. Artificial intelligence techniques can further integrate multimodal MRI data to enable early prediction and individualized assessment of RIBI. This review summarizes recent advances in the application of multimodal MRI in RIBI associated with nasopharyngeal carcinoma.

Objective To develop an artificial intelligence (AI) model based on contrast-enhanced T₁-weighted (CE-T₁WI) and T₂ FLAIR MRI, and to validate and evaluate its diagnostic performance and clinical value in differentiating high-grade glioma (HGG) from brain metastasis. Methods A total of 272 patients with brain tumors confirmed by surgical pathology were retrospectively enrolled, including 143 cases of HGG and 129 cases of brain metastasis. Four radiologists with different levels of experience [two junior (2-3 years) and two mid-level (5-8 years)] were randomly assigned to two groups，AI-assisted test group with 2 radiologists +AI，and a non-AI control group with 2 radiologists only. All radiologists independently interpreted the MRI images of all patients in two rounds using a crossover design, including an AI-assisted test group and a non-AI control group, with a 3-week washout period between readings. Using pathology as the reference standard, diagnostic performance was compared using the DBMH method. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated, and the performance of the AI model was compared with that of junior and mid-level radiologists. Results The AI model achieved a significantly higher AUC of 0.975 (95%CI: 0.956-0.993) for classification, with a sensitivity of 97.20%, specificity of 97.67%, and accuracy of 97.43%. For differentiating HGG from brain metastasis, the AUCs of the test group and control group were 0.934 (95%CI: 0.909-0.958) and 0.707 (95%CI: 0.645-0.768), respectively. Sensitivity was 98.08% versus 83.22%, specificity was 69.19% versus 52.13%, and accuracy was 84.38% versus 68.47%, respectively. Diagnostic performance in the AI-assisted test group was superior to that in the control group for both junior and mid-level radiologists. Conclusion The neural network-based AI model demonstrates excellent diagnostic performance in differentiating high-grade glioma from brain metastasis. It can improve the diagnostic accuracy of radiologists and provide valuable support for clinical diagnostic and therapeutic decision-making.